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README.md

@@ -14,41 +14,42 @@ tags:
 - benchmark
 - kdd-2026
 - nanoparticles
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: train.parquet
+  - split: id_test
+    path: id_test.parquet
+  - split: ood_test
+    path: ood_test.parquet
 ---
 
 # RADII: Radius-Resolved Benchmark of Nanoparticle Structures
 
-**RADII** measures where graph generative models start to fail as the structures they
-generate grow larger. Radius is treated as a continuous scaling knob from
-in-distribution to out-of-distribution; the dataset spans ten materials and 25 radii
-with leakage-free splits.
+**RADII** measures where graph generative models start to fail as the structures
+they generate grow larger. Radius is treated as a continuous scaling knob from
+in-distribution to out-of-distribution; the dataset spans 10 materials and 25
+radii with leakage-free splits.
 
 - **Paper:** *How Far Can You Grow? Characterizing the Extrapolation Frontier of Graph Generative Models for Materials Science* — KDD '26
-- **Code:** [github.com/KurbanIntelligenceLab/RADII](https://github.com/KurbanIntelligenceLab/RADII) (MIT)
+- **Code (models, trainer, generation pipeline):** [github.com/KurbanIntelligenceLab/RADII](https://github.com/KurbanIntelligenceLab/RADII) (MIT)
 - **Archival DOI:** [10.5281/zenodo.20431021](https://doi.org/10.5281/zenodo.20431021) (CC-BY-4.0)
 
 ## Quick start
 
-This dataset uses a custom loader that downloads the seed (3 MB) from Zenodo
-and regenerates the full ~12 GB benchmark locally on first use. **First call
-takes minutes to ~1 hour depending on hardware; subsequent calls are instant.**
-
 ```bash
-pip install datasets "radii @ git+https://github.com/KurbanIntelligenceLab/RADII"
+pip install datasets
 ```
 
 ```python
 from datasets import load_dataset
 
-ds = load_dataset("KurbanIntelligenceLab/RADII", split="train", trust_remote_code=True)
-print(ds.features, len(ds), ds[0])
+ds = load_dataset("KurbanIntelligenceLab/RADII", split="train").with_format("torch")
+print(ds.features, len(ds), ds[0]["material"], ds[0]["num_atoms"])
 ```
 
-`with_format("torch")` works for one-line PyTorch integration:
-
-```python
-ds = ds.with_format("torch")
-```
+That's it — no `trust_remote_code`, no Zenodo download, no regeneration.
 
 ## Splits
 
@@ -58,8 +59,8 @@ ds = ds.with_format("torch")
 | `id_test` | 13,500 | 6 values: 11, 13, 15, 17, 19, 21 (interleaved, unseen orientations) |
 | `ood_test` | 13,480 | 4 values: 6, 7, 29, 30 (strictly outside training range) |
 
-The OOD test structures are ~59% smaller and ~24% larger than the smallest and largest
-training structures (atom counts 33–11,298 vs train 81–9,148).
+OOD test structures are ~59% smaller and ~24% larger than the smallest and
+largest training structures (atom counts 33–11,298 vs train 81–9,148).
 
 ## Materials (10)
 
@@ -80,13 +81,75 @@ Ag, Au, CH₃NH₃PbI₃, Fe₂O₃, MoS₂, PbS, SnO₂, SrTiO₃, TiO₂, ZnO.
 | `cell_pos` | list[list[float32]] | Unit-cell basis positions (per-material) |
 | `cell_z` | list[int8] | Unit-cell basis atomic numbers |
 
-## Why a custom loader?
+## Analysis recipes (per-radius, per-material, cross-split)
+
+The metadata columns are first-class, so slicing and grouping are one-liners:
+
+```python
+from datasets import load_dataset
+
+# All three splits at once
+ds_all = load_dataset("KurbanIntelligenceLab/RADII")
+
+# Per-radius slice — every train sample at R=12
+r12 = ds_all["train"].filter(lambda x: x["radius"] == 12)
+
+# Per-material grouping
+materials = ds_all["train"].unique("material")  # ['Ag', 'Au', ...]
+ag_ood    = ds_all["ood_test"].filter(lambda x: x["material"] == "Ag")
+
+# Cross-split: atom counts by radius
+import pandas as pd
+for split in ("train", "id_test", "ood_test"):
+    df = ds_all[split].select_columns(["radius", "num_atoms"]).to_pandas()
+    print(split, df.groupby("radius")["num_atoms"].agg(["mean", "min", "max"]))
+
+# Atom-count envelope check (replicates the paper's 59% / 24% statistic)
+train_min, train_max = ds_all["train"]["num_atoms"], ds_all["train"]["num_atoms"]
+print("train atom-count range:", min(train_min), "to", max(train_max))
+print("OOD  atom-count range:", min(ds_all["ood_test"]["num_atoms"]),
+      "to", max(ds_all["ood_test"]["num_atoms"]))
+```
+
+## PyTorch collate (variable-length `pos`/`z`)
+
+```python
+import torch
+from torch.utils.data import DataLoader
+
+def collate(batch):
+    return {
+        "pos":         [torch.as_tensor(b["pos"]) for b in batch],
+        "z":           [torch.as_tensor(b["z"])   for b in batch],
+        "cell_matrix": torch.stack([torch.as_tensor(b["cell_matrix"]) for b in batch]),
+        "material":    [b["material"] for b in batch],
+        "radius":      torch.tensor([b["radius"] for b in batch]),
+    }
+
+loader = DataLoader(ds, batch_size=32, shuffle=True, collate_fn=collate)
+```
+
+For PyTorch Geometric, the `pos`/`z` lists drop straight into
+`torch_geometric.data.Batch.from_data_list(...)`.
+
+## Models
+
+The 5 baselines from the paper (ADiT, CDVAE, DiffCSP, FlowMM, MatterGen) live
+in the GitHub repo's `radii.models` namespace and follow the standard
+HuggingFace model interface (`PyTorchModelHubMixin`):
+
+```python
+from radii.models import ADiTUnitCell
+from radii.train_config import ModelConfig
+
+m = ADiTUnitCell(**ModelConfig.ADiT.to_dict())
+m.save_pretrained("./my_adit")
+m2 = ADiTUnitCell.from_pretrained("./my_adit")
+# m.push_to_hub("my-username/my-radii-adit") also works
+```
 
-The benchmark is deterministic from a 3 MB seed archive. Rather than duplicating
-~12 GB of derived data on this Hub, the loader fetches the seed from the citeable
-Zenodo record and regenerates locally — keeping a single source of truth and
-making the reproducibility story explicit. The HF datasets cache makes subsequent
-calls fast.
+See the GitHub README for training (`python -m radii.train --model adit`) and
+override flags (`--epochs`, `--lr`, `--from-checkpoint`, `--eval-only`).
 
 ## Citation
 
@@ -102,4 +165,4 @@ calls fast.
 ## License
 
 CC-BY-4.0. See [10.5281/zenodo.20431021](https://doi.org/10.5281/zenodo.20431021)
-for the archival record terms. The accompanying code is MIT-licensed at the GitHub repo.
+for the archival record terms. Accompanying code is MIT-licensed at the GitHub repo.